Metabolomics: Markers of Drug-Induced Liver Injury(RMI)

描述（由申请人提供）： 开发用于临床用途的新药实体的成本是巨大的，并且在药物开发的后期阶段受到失败的极大影响。早期从开发管道中删除可能在人类治疗剂量下有毒的候选药物对药物开发成本产生巨大影响。肝损伤是将药物从市场上撤下或增加安全警报的主要原因之一。该提案的一个目的是确定一组尿中排泄的内源性代谢物，可用于筛选药物诱导的肝损伤，作为早期标志物。该提案的第二个目的是更深入地了解反映肝损伤特定机制的标志物。这两个目标都有可能更好地定义临床前使用的敏感标志物，并为患者群体开发标志物提供潜力。将开发给予溶剂、无作用水平或药物诱导肝损伤水平的氯贝特、丙戊酸、异烟肼、苯妥英和对乙酰氨基酚的大鼠的肝脏和尿液的NMR和GC-MS代谢组学特征。除代谢组学特征外，还将获得肝损伤的常规指标（肝脏重量、血清酶升高和组织病理学）。将对这些药物的尿液和肝脏代谢组学特征进行简化和分析，以提供预测每种药物反应测量值的信号模式。此外，将使用单个药物预测模式的联合，根据给药药物、剂量水平、暴露持续时间和与不良反应的相关性区分所有组。用于识别信号子集的方法将通过脱落一法进行交叉验证。将使用GC-MS、NMR和LC-MS/MS方法鉴定定义这些模式的信号，然后将其分配至生物化学途径，以定义标记物谱与作用模式的相关性。

DESCRIPTION (provided by applicant): The cost of developing new drug entities for clinical use is substantial, and is greatly impacted by failure during the later stages of drug development. The early removal from the development pipeline of drug candidates that are likely to be toxic at therapeutic doses in humans has a huge impact on the cost of drug development. Liver injury is one of the major reasons for removal of a drug from the market, or addition of safety alerts. One aim of this proposal is to identify a set of endogenous metabolites excreted in urine that can be used to screen, as an early marker, for drug-induced liver injury. A second aim of this proposal is to gain more insight into markers that are reflective of specific mechanisms of liver injury. Both aims have the potential of better defining sensitive markers for pre-clinical use as well as provide potential for development of markers for patient populations. NMR and GC-MS metabolomic profiles will be developed for liver and urine from rats administered vehicle, no-effect levels, or drug induced-liver injury levels of clofibrate, valproic acid, isoniazid, phenytoin, and acetaminophen. Conventional measures of liver injury (liver weights, elevation in serum enzymes, and histopathology) will be obtained in addition to metabolomic profiles. The urine and liver metabolomics profiles for these drugs will be reduced and analyzed to provide the pattern of signals that are predictive of the response measurements for each drug. In addition, the union of the predictive patterns for individual drugs will be used to differentiate all groups based on the drug administered, dose level, exposure duration, and correlation with adverse response. The method used to identify the sub-set(s) of signals will be cross-validated by the drop-one-out approach. The signals defining these patterns will be identified using GC-MS, NMR, and LC-MS/MS methods and then assigned to biochemical pathways for defining the relevancy of the marker profiles to mode of action.